1. Field of the Invention
This invention relates to a method and system for automated processing of medical tomographic images using feature-analysis techniques, and more particularly, to an automated method and system for the detection of lesions in computed tomographic (CT) scan images of the lungs.
2. Discussion of the Background
The cure of lung cancer depends upon detection at an early stage while the tumor is still small and localized. If the cancer is detected in this localized stage, the five-year survival rate is approximately 80% as opposed to an otherwise 10% survival rate. The detection of cancerous lung nodules in chest radiographs and CT images is one of the more difficult tasks performed by radiologists.
Conventional interpretation of CT scans of the thorax is a time-consuming task for radiologists, requiring a systematic visual search of up to 80 images (40 "lung" images and 40 "soft tissue" images). In addition, when a possible nodule is located in one CT image, the radiologist frequently must perform visual correlation of the image data with that of other images (sections), in order to eliminate the possibility that the "nodule" actually represents a blood vessel seen in cross section.
At surgery, it is common for more pulmonary nodules to be found than were located by CT. Nodules may be missed in CT images due to factors such as a failure to perform the necessary systematic search, or an understandable inability to assimilate the vast amount of information contained in the multiple images in a CT examination. A computerized scheme for the detection of nodules is especially important in the case of searching for a solitary nodule.
Although no generalized scheme for automatically segmenting organs has been proposed, various investigations of knowledge-based segmentation of specific organs have been described in the literature. Karssemeijer et al. in "Recognition of organs in CT-image sequences: A model guided approach," Computers and Biomed. Res., 21, 434-438 (1988), used a Markov random field image model to segment the spleen in abdominal CT scans. Shani applied generalized-cylinder organ models for recognition of 3-D structure in abdominal CT (Understanding 3-D images: Recognition of abdominal anatomy from CAT scans, UMI research Press, Ann Arbor, 1983). Stiehl ("Model-guided labeling of CSF-cavities in cranial computed tomograms," in Computer Assisted Radiology '85, edited H. U. Lemke et al., Springer-Verlag, Berlin, 1985) and Badran et al. ("Patient realignment in MRI of the head: an algorithm using mathematical morphology for feature extraction," J. Biomed. Eng., 12 (2), 139-142 (1990)) described techniques for extracting brain features from CT and MRI, respectively. Levin et al. investigated detectability of soft-tissue tumors using multi-spectral feature space classification based on multiple MR sequences ("Musculoskeletal tumors: improved depiction with linear combinations of MR images," Radiology 163, 545-549, 1987). Of these approaches, none used a multi-gray-level thresholding and decision tree (for comparison and correlation) to detect lesions of varying subtlety. In addition, none used comparison between CT sections (i.e., multiple cross-sectional sections) to aid in distinguishing lesions from normal anatomy (such as blood vessels).